{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scipy import interpolate\n",
    "import numpy as np\n",
    "import arcpy\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = arcpy.da.FeatureClassToNumPyArray(\"../data/weather.shp\",\n",
    "                                         [\"SHAPE@X\",\"SHAPE@Y\",\"max\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd = pandas.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SHAPE@X</th>\n",
       "      <th>SHAPE@Y</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.290045e+07</td>\n",
       "      <td>4.704248e+06</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.283901e+07</td>\n",
       "      <td>4.635198e+06</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.285633e+07</td>\n",
       "      <td>4.613838e+06</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.295710e+07</td>\n",
       "      <td>4.855773e+06</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.307018e+07</td>\n",
       "      <td>4.810230e+06</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        SHAPE@X       SHAPE@Y  max\n",
       "0  1.290045e+07  4.704248e+06   15\n",
       "1  1.283901e+07  4.635198e+06   14\n",
       "2  1.285633e+07  4.613838e+06   13\n",
       "3  1.295710e+07  4.855773e+06   14\n",
       "4  1.307018e+07  4.810230e+06   15"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd = pd.sort_values(by =\"SHAPE@X\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(pd[\"SHAPE@X\"].tolist())\n",
    "y = np.array(pd[\"max\"].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "170"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(0,170)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "170"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Program Files\\ArcGIS\\Pro\\bin\\Python\\envs\\arcgispro-py3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: `spline` is deprecated!\n",
      "spline is deprecated in scipy 0.19.0, use Bspline class instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "D:\\Program Files\\ArcGIS\\Pro\\bin\\Python\\envs\\arcgispro-py3\\lib\\site-packages\\ipykernel_launcher.py:2: DeprecationWarning: `spline` is deprecated!\n",
      "spline is deprecated in scipy 0.19.0, use Bspline class instead.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "f = interpolate.spline(x,y,np.linspace(10, 50, 400))\n",
    "f2 = interpolate.spline(x,y,np.linspace(10, 50, 400),order=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(18, 6))\n",
    "ax.scatter(x(10,50), f[350:len(f)])\n",
    "ax.plot(range(350,len(f)), f[350:len(f)], label= \"cubic\")\n",
    "ax.plot(range(350,len(f2)), f2[350:len(f2)], label= \"cubic\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
